In psychology, cognitive flow is a state of mind involving complete attention with a sense of enjoyment. A person's skill and the challenge of a task together result in different emotional states. When skill is too low and the task too challenging, people become anxious. Alternatively, if the task is too easy and skill is comparatively higher, people become bored. However, when the skill and the challenge are relatively proportional, people enter in a flow state, i.e. state of focused concentration and enjoyment.
It is very challenging to provide a learning experience to a person such as a student or a working professional in which a steady cognitive flow state is maintained i.e. which is meaningful, motivated and at the same time enjoyable in nature. This often affects the students who cannot learn or do not want to learn due to lack of engagement or guidance. The same is also true for the working professional in industries. It is very necessary to provide steady flow state to a learner. Further, it is also important to know the mental state of the person in order to maintain the optimum level of performance.
The mental state of an individual varies according to their IQ levels, task difficulties or other psychological or environmental reasons. The skill level of an individual is directly related to his or her IQ level and is treated as the prior knowledge of the individual. On the other hand, the challenge of the task is synonymous to the task difficulty level.
There are different approaches for measuring the flow state mainly indirect and direct approach. Indirect approach involves namely, (i) semi-structured interviews—for measuring a qualitative performance, (ii) questionnaires—flow state questionnaires/scales used to describe user experience and performance, (iii) experience sampling method—objective is to measure flow and other states of consciousness occurring in activities encountered in everyday life. The other indirect approach involve flow measurement in different domains like piano playing, video-game, online games, social networking sites, e-commerce business etc. These indirect approaches seem to be feasible and less complex, but they are not reliable enough.
The direct approaches involve analyzing the brain signals captured using techniques like functional Magnetic Resonance Imaging (fMRI), functional Near Infra-Red (fNIR) etc. Currently, Electroencephalography (EEG) is extensively being used in educational tasks through the advent of Brain Computer Interface (BCI) technology. In a research, greater left temporal alpha activity was noticed when compared to that of right temporal lobe affecting the performance associated with flow. In conjunction to this, the mid beta activity and theta activity also have an effect on performance whereas there was no significant results with respect to delta waveforms. In higher alpha activity coupled with lower beta activity is found to be characterized for flow state. Recently low cost devices are being used for analyzing the effect of various elementary cognitive tasks. Some of these works also suggested using other physiological responses like GSR and heart rate for assessing the flow state. The main problem of using multichannel physiological sensors is that, the results obtained from the multiple sensors need to be fused using an appropriate mechanism.
Prior attempts at such EEG measurements, however, have not been fruitful because of two major shortcomings. First, there was the failure to measure brain activity while the subject performed a task taxing the subject's mental processes, such as working memory, that are highly related to overall performance. Merely recording brain activity while the subject sits idly, watching a meaningless flashing light, or performing a task not requiring her or his full attention is insufficient to produce patterns of brain activity characterizing changes in an individual's overall performance over an extended time period. Second, there was a reliance on single, overly simplistic measures of brain function derived from theoretical constructs without sufficient support from empirical data.
Bayesian network is becoming an increasingly popular technique to model uncertain and complex domains. Unlike classical statistical models, BN allow the introduction of prior knowledge into models. This prevents extraneous data to be considered which might alter desired results. Bayesian network uses the concept of conditional probability which is proven to be very useful in applications to the real world problem domain, where probability of occurrence of an event is conditionally dependent on the probability of occurrence of a previous event. Bayesian Network modelling has been used in the areas of medicine, document classification, information retrieval, image processing, decision support system, gaming, bioinformatics, gene analysis etc.